MAFnet creates a multimodal feature by concatenating the information coming from the visual and audio paths. We analyze the importance of using unimodal information versus multimodal information in the case of event recognition. We also test different fusion techniques present in the literature to determine the best fusion method: addition, concatenation, multimodal compact bilinear pooling (MCB) [1]} and the multimodal residual fusion (DMR) [2]}.
